
"""simple docstring"""


from collections.abc import Iterable
from typing import Generic, TypeVar

_lowercase				 =					TypeVar('''_T''')







class 					lowerCAmelCase_					(   Generic[_T]     ):



						'''simple docstring'''


						def __init__(			self  :   List[str]	,A_  :   Iterable[_T] | None = None   )  -> None:
							A     = list(iterable or []   )
							A     = []


						def __len__(			self  :   Optional[Any]   )  -> int:
							return len(self._stacka   ) + len(self._stacka   )


						def __repr__(			self  :   Tuple   )  -> str:
							return F'Queue({tuple(self._stacka[::-1] + self._stacka   )})'


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   _T   )  -> None:
							self._stacka.append(A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> _T:
							A     = self._stacka.pop
							A     = self._stacka.append

							if not self._stacka:
								while self._stacka:
									stacka_append(stacka_pop()   )

							if not self._stacka:
								raise IndexError('Queue is empty'   )
							return self._stacka.pop()


if __name__ == "__main__":
		from doctest import testmod

		testmod()







"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)